Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC 29466, USA.
Luxembourg Institute of Health, 1A-B, rue Thomas Edison, Strassen, L-1445 Luxembourg City, Luxembourg.
Int J Environ Res Public Health. 2020 Mar 5;17(5):1682. doi: 10.3390/ijerph17051682.
We propose a general approach to the analysis of multivariate health outcome data where geo-coding at different spatial scales is available. We propose multiscale joint models which address the links between individual outcomes and also allow for correlation between areas. The models are highly novel in that they exploit survey data to provide multiscale estimates of the prevalences in small areas for a range of disease outcomes. Results The models incorporate both disease specific, and common disease spatially structured components. The multiple scales envisaged is where individual survey data is used to model regional prevalences or risks at an aggregate scale. This approach involves the use of survey weights as predictors within our Bayesian multivariate models. Missingness has to be addressed within these models and we use predictive inference which exploits the correlation between diseases to provide estimates of missing prevalances. The Case study we examine is from the National Health Survey of Chile where geocoding to Province level is available. In that survey, diabetes, Hypertension, obesity and elevated low-density cholesterol (LDL) are available but differential missingness requires that aggregation of estimates and also the assumption of smoothed sampling weights at the aggregate level. The methodology proposed is highly novel and flexibly handles multiple disease outcomes at individual and aggregated levels (i.e., multiscale joint models). The missingness mechanism adopted provides realistic estimates for inclusion in the aggregate model at Provincia level. The spatial structure of four diseases within Provincias has marked spatial differentiation, with diabetes and hypertension strongly clustered in central Provincias and obesity and LDL more clustered in the southern areas.
我们提出了一种分析多变量健康结果数据的通用方法，其中提供了不同空间尺度的地理编码。我们提出了多尺度联合模型，这些模型解决了个体结果之间的联系，并且允许区域之间存在相关性。这些模型非常新颖，因为它们利用调查数据为一系列疾病结果的小区域提供了多尺度的流行率估计。结果，该模型结合了特定疾病和常见疾病的空间结构成分。所设想的多个尺度是指个体调查数据用于在聚合尺度上建模区域流行率或风险的情况。这种方法涉及在我们的贝叶斯多变量模型中使用调查权重作为预测因子。必须在这些模型中处理缺失值，我们使用预测推理，利用疾病之间的相关性来提供缺失流行率的估计值。我们检查的案例来自智利国家健康调查，其中提供了到省一级的地理编码。在该调查中，糖尿病、高血压、肥胖和低密度脂蛋白胆固醇升高（LDL）可用，但差异缺失要求在聚合水平上进行估计的聚合和平滑采样权重的假设。所提出的方法非常新颖，灵活地处理了个体和聚合水平上的多种疾病结果（即，多尺度联合模型）。所采用的缺失机制为在省级聚合模型中提供了现实的估计值。四个疾病在省内部的空间结构存在明显的空间差异，糖尿病和高血压在中部省份强烈聚集，肥胖和 LDL 在南部地区更为聚集。
